{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercício - Clustering\n",
    "### Aluno : Joel Ribeiro - 371822\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import das bibliotecas importantes\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]\n",
      " [ 5.4  3.9  1.7  0.4]\n",
      " [ 4.6  3.4  1.4  0.3]\n",
      " [ 5.   3.4  1.5  0.2]\n",
      " [ 4.4  2.9  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.4  3.7  1.5  0.2]\n",
      " [ 4.8  3.4  1.6  0.2]\n",
      " [ 4.8  3.   1.4  0.1]\n",
      " [ 4.3  3.   1.1  0.1]\n",
      " [ 5.8  4.   1.2  0.2]\n",
      " [ 5.7  4.4  1.5  0.4]\n",
      " [ 5.4  3.9  1.3  0.4]\n",
      " [ 5.1  3.5  1.4  0.3]\n",
      " [ 5.7  3.8  1.7  0.3]\n",
      " [ 5.1  3.8  1.5  0.3]\n",
      " [ 5.4  3.4  1.7  0.2]\n",
      " [ 5.1  3.7  1.5  0.4]\n",
      " [ 4.6  3.6  1.   0.2]\n",
      " [ 5.1  3.3  1.7  0.5]\n",
      " [ 4.8  3.4  1.9  0.2]\n",
      " [ 5.   3.   1.6  0.2]\n",
      " [ 5.   3.4  1.6  0.4]\n",
      " [ 5.2  3.5  1.5  0.2]\n",
      " [ 5.2  3.4  1.4  0.2]\n",
      " [ 4.7  3.2  1.6  0.2]\n",
      " [ 4.8  3.1  1.6  0.2]\n",
      " [ 5.4  3.4  1.5  0.4]\n",
      " [ 5.2  4.1  1.5  0.1]\n",
      " [ 5.5  4.2  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.   3.2  1.2  0.2]\n",
      " [ 5.5  3.5  1.3  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 4.4  3.   1.3  0.2]\n",
      " [ 5.1  3.4  1.5  0.2]\n",
      " [ 5.   3.5  1.3  0.3]\n",
      " [ 4.5  2.3  1.3  0.3]\n",
      " [ 4.4  3.2  1.3  0.2]\n",
      " [ 5.   3.5  1.6  0.6]\n",
      " [ 5.1  3.8  1.9  0.4]\n",
      " [ 4.8  3.   1.4  0.3]\n",
      " [ 5.1  3.8  1.6  0.2]\n",
      " [ 4.6  3.2  1.4  0.2]\n",
      " [ 5.3  3.7  1.5  0.2]\n",
      " [ 5.   3.3  1.4  0.2]\n",
      " [ 7.   3.2  4.7  1.4]\n",
      " [ 6.4  3.2  4.5  1.5]\n",
      " [ 6.9  3.1  4.9  1.5]\n",
      " [ 5.5  2.3  4.   1.3]\n",
      " [ 6.5  2.8  4.6  1.5]\n",
      " [ 5.7  2.8  4.5  1.3]\n",
      " [ 6.3  3.3  4.7  1.6]\n",
      " [ 4.9  2.4  3.3  1. ]\n",
      " [ 6.6  2.9  4.6  1.3]\n",
      " [ 5.2  2.7  3.9  1.4]\n",
      " [ 5.   2.   3.5  1. ]\n",
      " [ 5.9  3.   4.2  1.5]\n",
      " [ 6.   2.2  4.   1. ]\n",
      " [ 6.1  2.9  4.7  1.4]\n",
      " [ 5.6  2.9  3.6  1.3]\n",
      " [ 6.7  3.1  4.4  1.4]\n",
      " [ 5.6  3.   4.5  1.5]\n",
      " [ 5.8  2.7  4.1  1. ]\n",
      " [ 6.2  2.2  4.5  1.5]\n",
      " [ 5.6  2.5  3.9  1.1]\n",
      " [ 5.9  3.2  4.8  1.8]\n",
      " [ 6.1  2.8  4.   1.3]\n",
      " [ 6.3  2.5  4.9  1.5]\n",
      " [ 6.1  2.8  4.7  1.2]\n",
      " [ 6.4  2.9  4.3  1.3]\n",
      " [ 6.6  3.   4.4  1.4]\n",
      " [ 6.8  2.8  4.8  1.4]\n",
      " [ 6.7  3.   5.   1.7]\n",
      " [ 6.   2.9  4.5  1.5]\n",
      " [ 5.7  2.6  3.5  1. ]\n",
      " [ 5.5  2.4  3.8  1.1]\n",
      " [ 5.5  2.4  3.7  1. ]\n",
      " [ 5.8  2.7  3.9  1.2]\n",
      " [ 6.   2.7  5.1  1.6]\n",
      " [ 5.4  3.   4.5  1.5]\n",
      " [ 6.   3.4  4.5  1.6]\n",
      " [ 6.7  3.1  4.7  1.5]\n",
      " [ 6.3  2.3  4.4  1.3]\n",
      " [ 5.6  3.   4.1  1.3]\n",
      " [ 5.5  2.5  4.   1.3]\n",
      " [ 5.5  2.6  4.4  1.2]\n",
      " [ 6.1  3.   4.6  1.4]\n",
      " [ 5.8  2.6  4.   1.2]\n",
      " [ 5.   2.3  3.3  1. ]\n",
      " [ 5.6  2.7  4.2  1.3]\n",
      " [ 5.7  3.   4.2  1.2]\n",
      " [ 5.7  2.9  4.2  1.3]\n",
      " [ 6.2  2.9  4.3  1.3]\n",
      " [ 5.1  2.5  3.   1.1]\n",
      " [ 5.7  2.8  4.1  1.3]\n",
      " [ 6.3  3.3  6.   2.5]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 7.1  3.   5.9  2.1]\n",
      " [ 6.3  2.9  5.6  1.8]\n",
      " [ 6.5  3.   5.8  2.2]\n",
      " [ 7.6  3.   6.6  2.1]\n",
      " [ 4.9  2.5  4.5  1.7]\n",
      " [ 7.3  2.9  6.3  1.8]\n",
      " [ 6.7  2.5  5.8  1.8]\n",
      " [ 7.2  3.6  6.1  2.5]\n",
      " [ 6.5  3.2  5.1  2. ]\n",
      " [ 6.4  2.7  5.3  1.9]\n",
      " [ 6.8  3.   5.5  2.1]\n",
      " [ 5.7  2.5  5.   2. ]\n",
      " [ 5.8  2.8  5.1  2.4]\n",
      " [ 6.4  3.2  5.3  2.3]\n",
      " [ 6.5  3.   5.5  1.8]\n",
      " [ 7.7  3.8  6.7  2.2]\n",
      " [ 7.7  2.6  6.9  2.3]\n",
      " [ 6.   2.2  5.   1.5]\n",
      " [ 6.9  3.2  5.7  2.3]\n",
      " [ 5.6  2.8  4.9  2. ]\n",
      " [ 7.7  2.8  6.7  2. ]\n",
      " [ 6.3  2.7  4.9  1.8]\n",
      " [ 6.7  3.3  5.7  2.1]\n",
      " [ 7.2  3.2  6.   1.8]\n",
      " [ 6.2  2.8  4.8  1.8]\n",
      " [ 6.1  3.   4.9  1.8]\n",
      " [ 6.4  2.8  5.6  2.1]\n",
      " [ 7.2  3.   5.8  1.6]\n",
      " [ 7.4  2.8  6.1  1.9]\n",
      " [ 7.9  3.8  6.4  2. ]\n",
      " [ 6.4  2.8  5.6  2.2]\n",
      " [ 6.3  2.8  5.1  1.5]\n",
      " [ 6.1  2.6  5.6  1.4]\n",
      " [ 7.7  3.   6.1  2.3]\n",
      " [ 6.3  3.4  5.6  2.4]\n",
      " [ 6.4  3.1  5.5  1.8]\n",
      " [ 6.   3.   4.8  1.8]\n",
      " [ 6.9  3.1  5.4  2.1]\n",
      " [ 6.7  3.1  5.6  2.4]\n",
      " [ 6.9  3.1  5.1  2.3]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 6.8  3.2  5.9  2.3]\n",
      " [ 6.7  3.3  5.7  2.5]\n",
      " [ 6.7  3.   5.2  2.3]\n",
      " [ 6.3  2.5  5.   1.9]\n",
      " [ 6.5  3.   5.2  2. ]\n",
      " [ 6.2  3.4  5.4  2.3]\n",
      " [ 5.9  3.   5.1  1.8]]\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris_data, rotules = [iris.get('data'), iris.get('target')]\n",
    "print(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H7rrANQShp1WdIHpr++taXRDxVSdHOYjFDqAvsElKuUWGqypPB0bEoN8azabMjBILxEPY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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175e3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oetpxnHfzWThcdhwuO65EJ84EJw+OuwtXgpMNSzbFPE9Kyf6dVgbH0tkrubn3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a097c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = kmeans.predict(iris_data)\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(iris_data)\n",
    "\n",
    "reduced_data_df = pd.DataFrame(reduced_data)\n",
    "reduced_data_df[2] = predictions\n",
    "\n",
    "# k-means clusters\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=reduced_data_df[2])\n",
    "plt.show()\n",
    "\n",
    "# Original\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=rotules)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09m3bz9t3fcI3L02u+WCVE5JmiUOLewyt4QK0BvMQMXcjc16C/A+BcHPkbQjX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11761a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oetpxnHfzWThcdhwuO65EJ84EJw+OuwtXgpMNSzbFPE9Kyf6dVgbH0tkrubn3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a3b7160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=5, random_state=0).fit(iris_data)\n",
    "predictions = kmeans.predict(iris_data)\n",
    "\n",
    "reduced_data = PCA(n_components=2).fit_transform(iris_data)\n",
    "\n",
    "reduced_data_df = pd.DataFrame(reduced_data)\n",
    "reduced_data_df[2] = predictions\n",
    "\n",
    "# k-means clusters\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=reduced_data_df[2])\n",
    "plt.show()\n",
    "\n",
    "# Original\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(reduced_data_df[0], reduced_data_df[1], c=rotules)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
